Bootstrap-Based Improvements for Inference with Clustered Errors
Jonah Gelbach and
Douglas Miller ()
The Review of Economics and Statistics, 2008, vol. 90, issue 3, 414-427
Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
References: Add references at CitEc
Citations View citations in EconPapers (573) Track citations by RSS feed
Downloads: (external link)
http://www.mitpressjournals.org/doi/pdf/10.1162/rest.90.3.414 link to full text (application/pdf)
Access to full text is restricted to subscribers.
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2007)
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:tpr:restat:v:90:y:2008:i:3:p:414-427
Ordering information: This journal article can be ordered from
http://mitpress.mit. ... me.tcl?issn=00346535
Access Statistics for this article
The Review of Economics and Statistics is currently edited by Daron Acemoglu, George J. Borjas, Dani Rodrik and Julio J. Rotemberg
More articles in The Review of Economics and Statistics from MIT Press
Series data maintained by Kristin Waites ().